Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.     

Long-memory characteristics of urban roadside air quality

Carbon monoxide is a major contributor to air pollution in urban cities, particularly at the roadside. Hourly, monthly and seasonal mean carbon monoxide concentration data are collected from a roadside air monitoring station in Hong Kong over 7-years. The station is a few metres from a major intersection surrounded by tall buildings. In particular, hourly patterns of concentrations on different days of the week are investigated. The data show that hourly carbon monoxide concentrations resemble the traffic pattern of the area and tend to be lower in the summer. Using a seasonal autoregressive integrated moving average models shows that the daily traffic cycle strongly influences concentrations. Further, it is found that urban roadside carbon monoxide monitoring data exhibits a long-memory process, suggesting that a model incorporating long memory and seasonality effects is needed simulate urban roadside air quality.     

Comparison and evaluation of several mobile-source and line-source models in Israel

This study compares several transportation-related air quality models. Two line-source models were used: Caline4 (California Department of Transportation) and Hiway2 (US Environmental Protection Agency). Two mobile-source models, Mobile5b (US Environmental Protection Agency) and COPERT3 (European Environment Agency), along with real-world emission factors were used and evaluated as well. Model predictions of NO_x concentrations were compared to measured values at two sites in Israel, differing by fleet composition and physical layout (‘at-grade’ and a ‘cut/depressed’ road sections). The process indicated that emission factors generated by COPERT3 are the most appropriate for free flowing traffic situations in Israel. Predictions by both line-source models were similar when applied to ‘at-grade’ road sections. When applied to ‘cut/depressed’ sections, Hiway2 better predicted concentrations during unstable conditions, while Caline4 better predicted concentrations during stable conditions and peak concentrations.     

Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood

In this study, the effects of isolated traffic calming measures and area-wide calming schemes on air quality in a dense neighborhood were estimated using a combination of microscopic traffic simulation, emission, and dispersion modeling. Results indicated that traffic calming measures did not have as large an effect on nitrogen dioxide (NO₂) concentrations as the effect observed on nitrogen oxide (NO_x) emissions. Changes in emissions resulted in highly disproportional changes in pollutant levels due to daily meteorological conditions, road geometry and orientation with respect to the wind. Average NO₂ levels increased between 0.1% and 10% with respect to the base-case while changes in NO_x emissions varied between 5% and 160%. Moreover, higher wind speeds decreased NO₂ concentrations on both sides of the roadway. Among the traffic calming measures, speed bumps produced the highest increases in NO₂ levels.     

The impact of Low Emission Zones on particulate matter concentration and public health

A common policy for reducing particulate matter concentrations in the European Union is the introduction of Low Emission Zones (LEZs), which may only be entered by vehicles meeting predefined emission standards. This paper examines the effectiveness of LEZs for reducing PM₁₀ levels in urban areas in Germany and quantifies the associated health impacts from reduced air pollution within the zones. We employ a fixed effects panel data model for daily observations of PM₁₀ concentrations from 2000 to 2009 and control, inter alia, for local meteorological conditions and traffic volume. We apply the regression outputs to a concentration response function derived from the epidemiological literature to calculate associated health impacts of the introduction of LEZs in 25 German cities with 3.96 million inhabitants. Associated uncertainties are accounted for in Monte-Carlo simulations. It is found that the introduction of LEZs has significantly reduced inner city PM₁₀ levels. We estimate the total mean health impact from reduced air pollution in 2010 due to the introduction of stage 1 zones to be ∼760 million EUR in the 25 LEZ cities in the sample, whereas total mean health benefits are ∼2.4 billion EUR for the more stringent stage 2 zones when applied in the same cities.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

Dense network traffic models,Travel time reliability and traffic management. I: General introduction

This paper is the first of a pair of papers discussing two main themes concerning dense network modelling. These themes are: (1) the changing nature of traffic management technology and the underlying objectives behind traffic management practice, and (2) the use of measures of network reliability in models, especially as an element of the evaluation of alternative network configurations. This paper considers the role and function of dense network models, and their relationships with other transport network models on the basis of the hierarchical view of models. It then explores the peculiar characteristics of dense network models and provides examples of typical models. Changing needs for modelling capabilities, in terms of the evolution of traffic management technology and practice are discussed, pointing the way to future model developments. The companion paper develops the second theme through the definition and application of a set of network reliability indices that may be applied to different trip movements.     

Influence of coordinated traffic lights parameters on roadside pollutant concentrations

The paper examines the effects of coordinated traffic lights on CO and C₆H₆ roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C₆H₆ roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time.     

Norm approximation method for handling traffic count inconsistencies in path flow estimator

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin–destination (O–D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L_p-PFE models for estimating consistent path flows and O–D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L_p-PFE models. In addition, the proposed L_p-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.     

Local online kernel ridge regression for forecasting of urban travel times

Accurate and reliable forecasting of traffic variables is one of the primary functions of Intelligent Transportation Systems. Reliable systems that are able to forecast traffic conditions accurately, multiple time steps into the future, are required for advanced traveller information systems. However, traffic forecasting is a difficult task because of the nonlinear and nonstationary properties of traffic series. Traditional linear models are incapable of modelling such properties, and typically perform poorly, particularly when conditions differ from the norm. Machine learning approaches such as artificial neural networks, nonparametric regression and kernel methods (KMs) have often been shown to outperform linear models in the literature. A bottleneck of the latter approach is that the information pertaining to all previous traffic states must be contained within the kernel, but the computational complexity of KMs usually scales cubically with the number of data points in the kernel. In this paper, a novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model. Exploiting the observation that traffic data exhibits strong cyclic patterns characterised by rush hour traffic, LOKRR makes use of local kernels with varying parameters that are defined around each time point. This approach has 3 advantages over the standard single kernel approach: (1) It allows parameters to vary by time of day, capturing the time varying distribution of traffic data; (2) It allows smaller kernels to be defined that contain only the relevant traffic patterns, and; (3) It is online, allowing new traffic data to be incorporated as it arrives. The model is applied to the forecasting of travel times on London’s road network, and is found to outperform three benchmark models in forecasting up to 1 h ahead.     

Impact of public transport and non-motorized transport infrastructure on travel mode shares,energy, emissions and safety: Case of Indian cities

Current modal share in Indian cities is in favor of non-motorized transport (NMT) and public transport (PT), however historical trends shows decline in its use. Existing NMT and PT infrastructure in Indian cities is of poor quality resulting in increasing risk from road traffic crashes to these users. It is therefore likely that the current NMT and PT users will shift to personal motorized vehicles (PMV) as and when they can afford it. Share of NMT and PT users can be retained and possibly increased if safe and convenient facilities for them are created. This shall also have impact on reducing environment impacts of transport system.We have studied travel behavior of three medium size cities – Udaipur, Rajkot and Vishakhapatnam. Later the impact of improving built environment and infrastructure on travel mode shares, fuel consumption, emission levels and traffic safety in Rajkot and Vishakhapatnam are analyzed. For the purpose three scenarios are developed – improving only NMT infrastructure, improving only bus infrastructure and improving both NMT and bus infrastructure.The study shows the strong role of NMT infrastructure in both cities despite geographical dissimilarities. The scenario analysis shows maximum reduction in CO₂ emissions is achieved when both PT and NMT infrastructure are improved. Improvement in safety indicator is highest in this scenario. Improving only PT infrastructure may have marginal effect on overall reduction of CO₂ emissions and adverse effects on traffic safety. NMT infrastructure is crucial for maintaining the travel mode shares in favor of PT and NMT in future.     

Bicycle mode share in China: a city-level analysis of long term trends

In the past 15 years, cities in China have experienced big changes in socioeconomic and traffic conditions, resulting in a long term change in bicycle use. This study aims to quantify the changes in bicycle mode share in Chinese cities and explore the potential causes. Based on data from 51 cities, it is found that bicycle mode share at the city level decreased gradually in the past. Conventional bicycle mode share decreased with a rate of 3 % per year, but electric bicycle mode share increased with a rate of 2 % per year. The correlations between city features such as demographics and built environments and bicycle mode share at different city sizes are compared. The generalized linear models are estimated to relate the changes in the share of different trip modes to various city-level factors. The results show that the bicycle mode share in a city is impacted by factors including city area size, population density, number of cars, percentage of local road mileage among all roads, and trip purpose. Possible reasons for the changes in bicycle uses in Chinese cities are explored and policy implications are discussed.     

Simulating the environmental effects of isolated and area-wide traffic calming schemes using traffic simulation and microscopic emission modeling

This study focuses on the development of a microscopic traffic simulation and emission modeling system which aims at quantifying the effects of different types of traffic calming measures on vehicle emissions both at a link-level and at a network-level. It also investigates the effects of isolated traffic-calming measures at a corridor level and area-wide calming schemes, using a scenario analysis. Our study is set in Montreal, Canada where a traffic simulation model for a dense urban neighborhood is extended with capabilities for microscopic emission estimation. The results indicate that on average, isolated calming measures increase carbon dioxide (CO₂), carbon monoxide (CO), and nitrogen oxides (NO_x) emissions by 1.5, 0.3, and 1.5 %, respectively across the entire network. Area-wide schemes result in a percentage increase of 3.8 % for CO₂, 1.2 % for CO, and 2.2 % for NO_x across the entire network. Along specific corridors where traffic calming measures were simulated, increases in emissions of up to 83 % were observed. We also account for the effect of different measures on traffic volumes and observe moderate decreases in areas that have undergone traffic calming. In spite of traffic flow reductions, total emissions do increase.     

A system based approach to develop hybrid model predicting extreme urban NOx and PM2.5 concentrations

One of the major drawbacks of conventional air quality models is their inability in accurately predicting extreme air pollutant concentrations. Hybrid modelling is one of the techniques that estimates/predicts the ‘entire range’ of the distribution of pollutant concentrations by combining the deterministic based models (capable in predicting average range) with suitable statistical (probability) distribution models (capable in predicting extreme range). This research paper describes system based approach in developing hybrid model to predict hourly averages as well as extreme percentile ranges of NO_x and PM_2.5 concentrations at two urban locations having complex traffic heterogeneity, highly variable tropical meteorology and different geographical characteristics. At one of the selected locations i.e. Delhi megacity, during winters, hybridization of AERMOD and Lognormal predicts NO_x and PM_2.5 concentrations satisfactorily with index of agreement ‘d’ values of 0.98–0.99, respectively; however, during summers, AERMOD-Log-logistic and AERMOD-Lognormal are best predicting NO_x and PM_2.5 concentrations with d values of 0.98–0.96, respectively. In another, i.e., Chennai, a coastal megacity, AERMOD-Lognormal predicts PM_2.5 concentrations satisfactorily with d values of 0.98 and 0.99 during winter and summer seasons, respectively. Further, hybrid model has also been used to evaluate regulatory compliance.     

Roadway and traffic characteristics for bicycling

The promotion of bicycle transportation includes the provision of suitable infrastructure for cyclists. In order to determine if a road is suitable for bicycling or not, and what improvements need to be made to increase the level of service for bicycles on specific situations, it is important to know how cyclists perceive the characteristics that define the roadway environment. The present paper describes research developed to define which roadway and traffic characteristics are prioritized by users and potential users in the evaluation of quality of roads for bicycling in urban areas of Brazilian medium-sized cities. A focus group discussion identified 14 attributes representing characteristics that describe the quality of roads for bicycling in Brazilian cities. In addition, an attitude survey was applied with individuals to assess their perception on the attributes, along with the importance given to each one of them. The results were analyzed through the Method of Successive Intervals Analysis, which allows the transformation of categorical data into an interval scale. The analysis suggests that both the roadway and traffic characteristics related to segments and those related to intersections are important to the survey respondents. The five most important attributes, in their opinion, are: (1) lane width; (2) motor vehicle speed; (3) visibility at intersections; (4) presence of intersections; and (5) street trees (shading). Therefore, the research suggests that to promote bicycle use in Brazilian medium-sized cities, these attributes must be prioritized.     

Environmental,functional and economic criteria for comparing “target roundabouts” with one- or two-level roundabout intersections

The article describes a criterion based on functional, environmental and economic aspects for comparing conventional roundabouts with innovative one- or two-level roundabouts. We compared the performances of eight roundabout types, differing in geometric layout, number of lanes and traffic flow regulation from each other, with regard to vehicle delays and CO₂, NO_x, PM_2.5 and PM₁₀ pollutant emissions. Recently-designed roundabouts – target roundabouts and flyover roundabouts – have also been studied for their undoubted practical interest. By means of closed-form capacity models and CORINAIR methodology, several traffic simulations were carried out to examine a typical annual traffic demand curve in a suburban context, three different distribution test matrices for traffic flows (ρ1, ρ2, ρ3) and maximum annual traffic flow values Q_max ranging between 1300 and 3300 veh/h.Estimating vehicle delays and annual pollutant emissions, along with construction and management costs, allowed obtaining overall costs for each roundabout examined, in function of traffic demand and several other parameters. Thanks to these analyses, we identified the roundabout types which best suit to each traffic condition.     

CO2 emissions in relation to street-network configuration and city size

The street-network efficiency of tens of British cities in relation to transport fuel consumption and CO₂ emissions are analyzed. The results show a strong linear positive correlation between length entropy and average street length, and a negative correlation between entropy and street density. Also, the results suggest that in a large city the street network is used more efficiently than in a small city, as indicated by the sublinear relations between city size (population) and the number of streets, total length of streets, and the area covered by the street network. The sublinear relation means that these parameters grow more slowly than the city size. By contrast, because a larger fraction of the street network is used at close to full capacity during considerable part of the time in a large city than a small one, the fuel consumption and the CO₂ emissions show a linear relation with city size and superlinear relation with total street length. The superlinear relation means that the CO₂ emissions increase faster than the total street length, a measure of the network size. Thus, large cities may be less energy efficient and environmentally friendly than small cities. In every city the street network needs to interconnect all the buildings, which requires a certain minimum size. In a small city, however, the network is used to a low capacity most of the time so that its relations to fuel consumption and the CO₂ emissions are more favorable than those in a large city.